Hypotheses

Check all hypotheses between traits and environmental drivers, and among traits

Big PCA

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Arthropods (above, herbivores)
SLA	++	D	disturbance favours fast-growing species	NA	more nutrients allow for ‘cheap’ leaves	NA	0.35	0.2 - 0.5	0.0000313	✅	0.36	0.13	0.10	0.35	0.2 - 0.5	✅
Seed_mass	–	D	disturbance selects for smaller seeds (= colonisation)	Diaz et al. 2016	NA	NA	-0.37	-0.52 - -0.22	0.0000000	✅	-0.16	-0.21	-0.28	-0.29	-0.45 - -0.14	✅
LDMC	–	D	Tougher = slow	NA	NA	NA	-0.38	-0.53 - -0.23	0.0000000	✅	-0.40	-0.43	-0.02	-0.36	-0.51 - -0.21	✅
LeafN	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	Diaz et al. 2016	0.49	0.35 - 0.63	0.0000000	✅	0.38	0.46	0.14	0.48	0.34 - 0.62	✅
Arthropods (above, omnicarnivores)
LeafP	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	NA	0.56	0.42 - 0.69	0.0000000	✅	0.34	0.45	0.26	0.53	0.39 - 0.66	✅
Root_tissue_density	–	D	NA	NA	conservative and/or ‘collaboration’ => if few nutrients, need for collaboration	Bergmann et al. 2020	-0.22	-0.38 - -0.06	0.0119654	✅	-0.26	-0.06	-0.04	-0.27	-0.42 - -0.11	✅
Arthropods (below, herbivores)
Aoc_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	0.05	-0.11 - 0.21	0.6465438	Inconclusive	0.04	-0.23	0.07	0.08	-0.08 - 0.25	Inconclusive
Aoc_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.33	0.18 - 0.48	0.0001175	✅	0.53	0.23	-0.12	0.32	0.17 - 0.48	✅
Ah_BodySize	+/-	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.24	-0.39 - -0.08	0.0074773	B	-0.30	-0.29	0.02	-0.26	-0.42 - -0.1	B
Arthropods (below, predators)
Ah_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.44	0.29 - 0.58	0.0000000	✅	0.24	0.40	0.24	0.28	0.13 - 0.44	✅
Ah_Generalism	++	NA	NA	NA	NA	NA	0.38	0.23 - 0.53	0.0000000	✅	0.04	0.31	0.31	0.39	0.24 - 0.54	✅
Bats
Ah_Generations	++	I	NA	NA	NA	NA	0.61	0.49 - 0.74	0.0000000	✅	0.61	0.39	0.15	0.54	0.4 - 0.68	✅
Aoc_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.05	-0.21 - 0.11	0.6465438	Inconclusive	-0.08	-0.02	0.03	-0.01	-0.17 - 0.15	Inconclusive
Aoc_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.10	-0.06 - 0.26	0.3099788	Inconclusive	0.20	0.05	-0.04	0.11	-0.05 - 0.27	Inconclusive
Birds (insectivorous)
Ah_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.28	-0.46 - -0.1	0.0060429	✅	-0.24	-0.32	-0.03	-0.25	-0.41 - -0.09	✅
Ah_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.18	-0.01 - 0.36	0.1060903	Inconclusive	-0.04	0.08	0.25	0.15	-0.01 - 0.32	Inconclusive
Ah_b_Generalism	++	NA	NA	NA	NA	NA	-0.02	-0.2 - 0.17	0.8792400	Inconclusive	0.11	0.06	-0.16	-0.09	-0.28 - 0.1	Inconclusive
Bi_Size	++	NA	more = fast	NA	NA	NA	0.26	0.1 - 0.42	0.0031663	✅	0.33	0.20	0.01	0.31	0.16 - 0.47	✅
Bi_Incub	++	NA	more = disturbance	NA	NA	NA	0.14	-0.02 - 0.3	0.1459274	Inconclusive	0.30	0.12	-0.09	0.18	0.02 - 0.34	✔
Butterflies
Bi_TOffsprings	–	I	Small = fast	NA	NA	NA	-0.14	-0.3 - 0.02	0.1449167	Inconclusive	-0.33	-0.14	0.12	-0.15	-0.31 - 0.01	Inconclusive
Bi_GenLength	–	NA	NA	NA	NA	NA	0.22	0.06 - 0.38	0.0133480	❌	0.34	0.19	-0.05	0.27	0.11 - 0.42	❌
Bi_AgeMax	–	I	Small = fast	NA	NA	NA	0.26	0.1 - 0.41	0.0042065	❌	0.31	0.21	0.00	0.30	0.15 - 0.46	❌
but_flight	++	NA	Early emergence favorable bc disturbance starts early	Börschig et al. 2013	NA	NA	0.41	0.27 - 0.56	0.0000000	✅	0.53	0.25	0.05	0.25	0.09 - 0.41	✅
but_GenYear	++	NA	High reproduction can compensate mortality due to disturbance	Börschig et al. 2013	NA	NA	0.19	0.02 - 0.35	0.0429267	✅	0.11	0.12	0.08	0.23	0.07 - 0.39	✅
Collembola
but_hibernation	++	NA	Later hibernation stage means butterflies ready before disturbance	NA	NA	NA	0.38	0.23 - 0.53	0.0000000	✅	0.41	0.24	0.09	0.25	0.09 - 0.41	✅
but_Size	++	NA	Larger wings = more dispersal	Börschig et al. 2013	NA	NA	-0.03	-0.2 - 0.13	0.7896000	Inconclusive	-0.05	-0.06	0.03	-0.13	-0.29 - 0.03	Inconclusive
but_Generalism	++	NA	NA	NA	High nutrients = low plant diversity, hence more generalists	NA	0.27	0.11 - 0.43	0.0024022	✅	0.07	0.03	0.24	0.28	0.12 - 0.44	✅
col_Sex	–	NA	NA	NA	NA	NA	0.01	-0.16 - 0.18	0.8792400	Inconclusive	-0.02	0.06	0.03	0.01	-0.16 - 0.17	Inconclusive
Microbes
col_Gen_per_Year	++	NA	Furca = escape disturbance	NA	NA	NA	-0.11	-0.27 - 0.06	0.3099788	Inconclusive	0.23	-0.07	-0.30	-0.09	-0.26 - 0.08	Inconclusive
col_Depth	++	NA	NA	NA	NA	NA	-0.03	-0.2 - 0.14	0.7932959	Inconclusive	0.00	-0.13	-0.01	-0.01	-0.18 - 0.16	Inconclusive
col_Size	–	NA	a/ more resources or b/ selected	NA	NA	NA	0.05	-0.12 - 0.22	0.6465438	Inconclusive	-0.22	0.00	0.24	0.04	-0.13 - 0.21	Inconclusive
mites_DaysAdult	–	NA	longer lifespan = slow	NA	NA	NA	-0.06	-0.23 - 0.11	0.6075830	Inconclusive	-0.10	-0.16	0.05	-0.05	-0.22 - 0.12	Inconclusive
mites_Mass	–	NA	Mass = size = slow	NA	NA	NA	-0.17	-0.34 - -0.01	0.0730850	✔	-0.09	-0.21	-0.09	-0.16	-0.32 - 0.01	Inconclusive
Mites
mites_Sex	–	I	Habitat openness = sexual repro, more LUI = more growth	NA	NA	NA	0.02	-0.15 - 0.19	0.8569040	Inconclusive	0.09	-0.02	-0.06	0.01	-0.16 - 0.18	Inconclusive
mites_Hab_spec	++	NA	more disturbance = more in the soil	NA	NA	NA	-0.04	-0.21 - 0.13	0.7023176	Inconclusive	-0.18	-0.09	0.10	-0.03	-0.2 - 0.14	Inconclusive
mites_Feed_spec	++	NA	NA	NA	NA	NA	0.10	-0.06 - 0.27	0.3099788	Inconclusive	0.14	0.08	-0.01	0.12	-0.04 - 0.29	Inconclusive
P_patho	++	NA	More bacteria = more bacterivores	NA	NA	NA	0.49	0.35 - 0.63	0.0000000	✅	0.33	0.37	0.21	0.46	0.32 - 0.61	✅
Pb_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.20	-0.36 - -0.04	0.0266815	✅	-0.36	-0.13	0.03	-0.27	-0.43 - -0.11	✅
Plants (AG)
Ps_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.44	-0.58 - -0.29	0.0000000	✅	-0.11	-0.39	-0.31	-0.31	-0.46 - -0.15	✅
mic_FB	–	D and I	Fungi slower than bacteria, linked to slow plant traits	NA	Fungi dominate in low-nutrient soils; associated with slow plants	de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019	-0.37	-0.52 - -0.22	0.0000000	✅	-0.22	-0.37	-0.18	-0.24	-0.4 - -0.08	✅
mic_Fpathotroph	++	NA	Fast= more parasites	check https://www.nature.com/articles/s41467-021-23605-y	Fast plant invest less in defenses, so more pathogens	NA	0.42	0.27 - 0.56	0.0000000	✅	0.19	0.47	0.22	0.31	0.15 - 0.46	✅
mic_O.C.ratio	–	NA	NA	NA	more oligo (Acido) than copio (Actibo, alphapto…) in slow	Check Neff 2015, Ramirez 2010, Fierer 2012	-0.23	-0.38 - -0.07	0.0114230	✅	0.09	-0.14	-0.27	-0.04	-0.2 - 0.12	Inconclusive
mic_Bvolume	+/-	D	a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations	Westoby et al. 2021	NA	NA	-0.45	-0.59 - -0.3	0.0000000	B	-0.19	-0.31	-0.28	-0.31	-0.46 - -0.15	B
Plants (BG)
mic_Bgenome_size	–	D	NA	NA	Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)	Leff et al. 2015; Konstantinidis (2004)	-0.30	-0.46 - -0.15	0.0004147	✅	-0.11	-0.42	-0.13	-0.04	-0.21 - 0.12	Inconclusive
Protists
bat_mass	–	NA	Large more sensitive to disturbance BUT large less sensitive to urbanisation??	Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018	NA	NA	-0.10	-0.26 - 0.06	0.3160146	Inconclusive	-0.07	-0.04	-0.05	-0.17	-0.33 - -0.01	✔
Protists bacterivores
bat_lifespan	NA	NA	NA	NA	NA	NA	-0.03	-0.19 - 0.14	0.7932959	Inconclusive	0.11	-0.13	-0.09	0.06	-0.1 - 0.23	Inconclusive
Protists predators
bat_offspring	++	NA	opposite to size	NA	NA	NA	-0.06	-0.22 - 0.1	0.5947589	Inconclusive	-0.09	0.12	-0.03	-0.16	-0.32 - 0.01	Inconclusive

Identification of strategy axes for each group

Plants, above- and below-ground

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Plants (AG)
SLA	++	D	disturbance favours fast-growing species	NA	more nutrients allow for ‘cheap’ leaves	NA	0.35	0.2 - 0.5	0.0000313	✅	0.36	0.13	0.10	0.35	0.2 - 0.5	✅
Seed_mass	–	D	disturbance selects for smaller seeds (= colonisation)	Diaz et al. 2016	NA	NA	-0.37	-0.52 - -0.22	0.0000000	✅	-0.16	-0.21	-0.28	-0.29	-0.45 - -0.14	✅
LDMC	–	D	Tougher = slow	NA	NA	NA	-0.38	-0.53 - -0.23	0.0000000	✅	-0.40	-0.43	-0.02	-0.36	-0.51 - -0.21	✅
LeafN	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	Diaz et al. 2016	0.49	0.35 - 0.63	0.0000000	✅	0.38	0.46	0.14	0.48	0.34 - 0.62	✅
LeafP	++	D	NA	NA	more nutrients allow for ‘cheap’ leaves	NA	0.56	0.42 - 0.69	0.0000000	✅	0.34	0.45	0.26	0.53	0.39 - 0.66	✅
Plants (BG)
Root_tissue_density	–	D	NA	NA	conservative and/or ‘collaboration’ => if few nutrients, need for collaboration	Bergmann et al. 2020	-0.22	-0.38 - -0.06	0.0119654	✅	-0.26	-0.06	-0.04	-0.27	-0.42 - -0.11	✅

## [1] "Plants, All"

## Registered S3 methods overwritten by 'car':
##   method                          from
##   influence.merMod                lme4
##   cooks.distance.influence.merMod lme4
##   dfbeta.influence.merMod         lme4
##   dfbetas.influence.merMod        lme4

Bacteria & fungi

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Microbes
mic_FB	–	D and I	Fungi slower than bacteria, linked to slow plant traits	NA	Fungi dominate in low-nutrient soils; associated with slow plants	de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019	-0.37	-0.52 - -0.22	0.0000000	✅	-0.22	-0.37	-0.18	-0.24	-0.4 - -0.08	✅
mic_Fpathotroph	++	NA	Fast= more parasites	check https://www.nature.com/articles/s41467-021-23605-y	Fast plant invest less in defenses, so more pathogens	NA	0.42	0.27 - 0.56	0.0000000	✅	0.19	0.47	0.22	0.31	0.15 - 0.46	✅
mic_O.C.ratio	–	NA	NA	NA	more oligo (Acido) than copio (Actibo, alphapto…) in slow	Check Neff 2015, Ramirez 2010, Fierer 2012	-0.23	-0.38 - -0.07	0.0114230	✅	0.09	-0.14	-0.27	-0.04	-0.2 - 0.12	Inconclusive
mic_Bvolume	+/-	D	a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations	Westoby et al. 2021	NA	NA	-0.45	-0.59 - -0.3	0.0000000	B	-0.19	-0.31	-0.28	-0.31	-0.46 - -0.15	B
mic_Bgenome_size	–	D	NA	NA	Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)	Leff et al. 2015; Konstantinidis (2004)	-0.30	-0.46 - -0.15	0.0004147	✅	-0.11	-0.42	-0.13	-0.04	-0.21 - 0.12	Inconclusive

Arthropods, above-ground

Herbivores

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Arthropods (above, herbivores)
Aoc_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	0.05	-0.11 - 0.21	0.6465438	Inconclusive	0.04	-0.23	0.07	0.08	-0.08 - 0.25	Inconclusive
Aoc_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.33	0.18 - 0.48	0.0001175	✅	0.53	0.23	-0.12	0.32	0.17 - 0.48	✅
Ah_BodySize	+/-	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.24	-0.39 - -0.08	0.0074773	B	-0.30	-0.29	0.02	-0.26	-0.42 - -0.1	B
Ah_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.44	0.29 - 0.58	0.0000000	✅	0.24	0.40	0.24	0.28	0.13 - 0.44	✅
Arthropods (above, omnicarnivores)
Ah_Generalism	++	NA	NA	NA	NA	NA	0.38	0.23 - 0.53	0.0000000	✅	0.04	0.31	0.31	0.39	0.24 - 0.54	✅
Ah_Generations	++	I	NA	NA	NA	NA	0.61	0.49 - 0.74	0.0000000	✅	0.61	0.39	0.15	0.54	0.4 - 0.68	✅
Arthropods (below, herbivores)
Aoc_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.05	-0.21 - 0.11	0.6465438	Inconclusive	-0.08	-0.02	0.03	-0.01	-0.17 - 0.15	Inconclusive
Aoc_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.10	-0.06 - 0.26	0.3099788	Inconclusive	0.20	0.05	-0.04	0.11	-0.05 - 0.27	Inconclusive
Ah_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.28	-0.46 - -0.1	0.0060429	✅	-0.24	-0.32	-0.03	-0.25	-0.41 - -0.09	✅
Arthropods (below, predators)
Ah_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.18	-0.01 - 0.36	0.1060903	Inconclusive	-0.04	0.08	0.25	0.15	-0.01 - 0.32	Inconclusive
Ah_b_Generalism	++	NA	NA	NA	NA	NA	-0.02	-0.2 - 0.17	0.8792400	Inconclusive	0.11	0.06	-0.16	-0.09	-0.28 - 0.1	Inconclusive

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Butterflies
but_flight	++	NA	Early emergence favorable bc disturbance starts early	Börschig et al. 2013	NA	NA	0.41	0.27 - 0.56	0.0000000	✅	0.53	0.25	0.05	0.25	0.09 - 0.41	✅
but_GenYear	++	NA	High reproduction can compensate mortality due to disturbance	Börschig et al. 2013	NA	NA	0.19	0.02 - 0.35	0.0429267	✅	0.11	0.12	0.08	0.23	0.07 - 0.39	✅
but_hibernation	++	NA	Later hibernation stage means butterflies ready before disturbance	NA	NA	NA	0.38	0.23 - 0.53	0.0000000	✅	0.41	0.24	0.09	0.25	0.09 - 0.41	✅
but_Size	++	NA	Larger wings = more dispersal	Börschig et al. 2013	NA	NA	-0.03	-0.2 - 0.13	0.7896000	Inconclusive	-0.05	-0.06	0.03	-0.13	-0.29 - 0.03	Inconclusive
but_Generalism	++	NA	NA	NA	High nutrients = low plant diversity, hence more generalists	NA	0.27	0.11 - 0.43	0.0024022	✅	0.07	0.03	0.24	0.28	0.12 - 0.44	✅

Predators

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Arthropods (above, omnicarnivores)
Aoc_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	0.05	-0.11 - 0.21	0.6465438	Inconclusive	0.04	-0.23	0.07	0.08	-0.08 - 0.25	Inconclusive
Aoc_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.33	0.18 - 0.48	0.0001175	✅	0.53	0.23	-0.12	0.32	0.17 - 0.48	✅
Arthropods (below, predators)
Aoc_b_BodySize	–	D	Smaller species evade disturbance more easily	Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016	NA	NA	-0.05	-0.21 - 0.11	0.6465438	Inconclusive	-0.08	-0.02	0.03	-0.01	-0.17 - 0.15	Inconclusive
Aoc_b_Dispersal	++	D	Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016	Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016	NA	NA	0.10	-0.06 - 0.26	0.3099788	Inconclusive	0.20	0.05	-0.04	0.11	-0.05 - 0.27	Inconclusive

Protists

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Protists
P_patho	++	NA	More bacteria = more bacterivores	NA	NA	NA	0.49	0.35 - 0.63	0.0000000	✅	0.33	0.37	0.21	0.46	0.32 - 0.61	✅
Protists bacterivores
Pb_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.20	-0.36 - -0.04	0.0266815	✅	-0.36	-0.13	0.03	-0.27	-0.43 - -0.11	✅
Protists predators
Ps_Size	–	NA	a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size	NA	NA	NA	-0.44	-0.58 - -0.29	0.0000000	✅	-0.11	-0.39	-0.31	-0.31	-0.46 - -0.15	✅

Birds

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Birds (insectivorous)
Bi_Size	++	NA	more = fast	NA	NA	NA	0.26	0.1 - 0.42	0.0031663	✅	0.33	0.20	0.01	0.31	0.16 - 0.47	✅
Bi_Incub	++	NA	more = disturbance	NA	NA	NA	0.14	-0.02 - 0.3	0.1459274	Inconclusive	0.30	0.12	-0.09	0.18	0.02 - 0.34	✔
Bi_TOffsprings	–	I	Small = fast	NA	NA	NA	-0.14	-0.3 - 0.02	0.1449167	Inconclusive	-0.33	-0.14	0.12	-0.15	-0.31 - 0.01	Inconclusive
Bi_GenLength	–	NA	NA	NA	NA	NA	0.22	0.06 - 0.38	0.0133480	❌	0.34	0.19	-0.05	0.27	0.11 - 0.42	❌
Bi_AgeMax	–	I	Small = fast	NA	NA	NA	0.26	0.1 - 0.41	0.0042065	❌	0.31	0.21	0.00	0.30	0.15 - 0.46	❌

Mites & collembola

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Mites
mites_DaysAdult	–	NA	longer lifespan = slow	NA	NA	NA	-0.06	-0.23 - 0.11	0.6075830	Inconclusive	-0.10	-0.16	0.05	-0.05	-0.22 - 0.12	Inconclusive
mites_Mass	–	NA	Mass = size = slow	NA	NA	NA	-0.17	-0.34 - -0.01	0.0730850	✔	-0.09	-0.21	-0.09	-0.16	-0.32 - 0.01	Inconclusive
mites_Sex	–	I	Habitat openness = sexual repro, more LUI = more growth	NA	NA	NA	0.02	-0.15 - 0.19	0.8569040	Inconclusive	0.09	-0.02	-0.06	0.01	-0.16 - 0.18	Inconclusive
mites_Hab_spec	++	NA	more disturbance = more in the soil	NA	NA	NA	-0.04	-0.21 - 0.13	0.7023176	Inconclusive	-0.18	-0.09	0.10	-0.03	-0.2 - 0.14	Inconclusive
mites_Feed_spec	++	NA	NA	NA	NA	NA	0.10	-0.06 - 0.27	0.3099788	Inconclusive	0.14	0.08	-0.01	0.12	-0.04 - 0.29	Inconclusive

## NULL

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Collembola
col_Sex	–	NA	NA	NA	NA	NA	0.01	-0.16 - 0.18	0.8792400	Inconclusive	-0.02	0.06	0.03	0.01	-0.16 - 0.17	Inconclusive
col_Gen_per_Year	++	NA	Furca = escape disturbance	NA	NA	NA	-0.11	-0.27 - 0.06	0.3099788	Inconclusive	0.23	-0.07	-0.30	-0.09	-0.26 - 0.08	Inconclusive
col_Depth	++	NA	NA	NA	NA	NA	-0.03	-0.2 - 0.14	0.7932959	Inconclusive	0.00	-0.13	-0.01	-0.01	-0.18 - 0.16	Inconclusive
col_Size	–	NA	a/ more resources or b/ selected	NA	NA	NA	0.05	-0.12 - 0.22	0.6465438	Inconclusive	-0.22	0.00	0.24	0.04	-0.13 - 0.21	Inconclusive

## NULL

Bats

Trait_short	Expected_direction	Direct/indirect	Disturbance	ref_dist	Resources	ref_res	Est	CI	Padj	Fit_exp	Mowing	Grazing	Fertil	Est_nocorr	CI_nocorr	Fit_exp_nocorr
Bats
bat_mass	–	NA	Large more sensitive to disturbance BUT large less sensitive to urbanisation??	Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018	NA	NA	-0.10	-0.26 - 0.06	0.3160146	Inconclusive	-0.07	-0.04	-0.05	-0.17	-0.33 - -0.01	✔
bat_lifespan	NA	NA	NA	NA	NA	NA	-0.03	-0.19 - 0.14	0.7932959	Inconclusive	0.11	-0.13	-0.09	0.06	-0.1 - 0.23	Inconclusive
bat_offspring	++	NA	opposite to size	NA	NA	NA	-0.06	-0.22 - 0.1	0.5947589	Inconclusive	-0.09	0.12	-0.03	-0.16	-0.32 - 0.01	Inconclusive

## NULL

Try to do a SEM

## 
##  Pearson's product-moment correlation
## 
## data:  PCA_pca$ind$coord[, 1] and env_data_lui[Plot %in% dd$Plot, ]$LUI
## t = 12.614, df = 148, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6324707 0.7890214
## sample estimates:
##       cor 
## 0.7197753

Run above-ground model, simple

## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"

##            cfi          rmsea rmsea.ci.upper            bic 
##          1.000          0.000          0.142       2441.121

## quartz_off_screen 
##                 2

Run below-ground model, simple

## lavaan 0.6-9 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        40
##                                                       
##                                                   Used       Total
##   Number of observations                           120         150
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                12.602
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.013
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.579    0.077    7.505    0.000    0.579    0.565
##   Protists_patho ~                                                            
##     LUI                     0.335    0.093    3.589    0.000    0.335    0.325
##     plant                   0.324    0.091    3.566    0.000    0.324    0.323
##   Microbes ~                                                                  
##     LUI                     0.300    0.082    3.659    0.000    0.300    0.302
##     plant                   0.431    0.080    5.397    0.000    0.431    0.446
##   Protists_bact ~                                                             
##     LUI                     0.282    0.112    2.510    0.012    0.282    0.289
##     plant                   0.108    0.115    0.940    0.347    0.108    0.114
##     Microbes               -0.332    0.113   -2.925    0.003   -0.332   -0.337
##     Protists_patho          0.077    0.100    0.771    0.441    0.077    0.081
##   Protists_sec ~                                                              
##     LUI                     0.184    0.100    1.835    0.066    0.184    0.200
##     Protists_bact          -0.023    0.080   -0.288    0.773   -0.023   -0.024
##     plant                   0.158    0.101    1.570    0.116    0.158    0.176
##     Microbes                0.166    0.102    1.619    0.105    0.166    0.178
##     Protists_patho          0.043    0.087    0.492    0.622    0.043    0.048
##   Mites ~                                                                     
##     LUI                     0.026    0.119    0.220    0.826    0.026    0.027
##     plant                   0.179    0.119    1.505    0.132    0.179    0.187
##     Microbes                0.204    0.121    1.693    0.090    0.204    0.207
##     Protists_sec           -0.111    0.107   -1.043    0.297   -0.111   -0.105
##     Protists_bact           0.000    0.093    0.005    0.996    0.000    0.000
##     Protists_patho         -0.196    0.102   -1.926    0.054   -0.196   -0.205
##   Coll ~                                                                      
##     LUI                     0.023    0.122    0.189    0.850    0.023    0.023
##     Microbes               -0.286    0.124   -2.301    0.021   -0.286   -0.286
##     plant                   0.175    0.122    1.430    0.153    0.175    0.181
##     Protists_sec           -0.059    0.110   -0.539    0.590   -0.059   -0.055
##     Protists_bact          -0.062    0.096   -0.649    0.516   -0.062   -0.061
##     Protists_patho          0.057    0.105    0.547    0.584    0.057    0.060
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.166    0.114    1.463    0.143    0.166    0.172
##     Mites                   0.097    0.092    1.065    0.287    0.097    0.099
##     Coll                   -0.061    0.088   -0.697    0.486   -0.061   -0.063
##     Protists_sec           -0.076    0.107   -0.708    0.479   -0.076   -0.072
##     plant                  -0.061    0.113   -0.540    0.589   -0.061   -0.065
##     Protists_patho          0.030    0.104    0.292    0.771    0.030    0.032
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.762    0.098    7.746    0.000    0.762    0.681
##    .Protists_patho    0.756    0.098    7.746    0.000    0.756    0.671
##    .Microbes          0.583    0.075    7.746    0.000    0.583    0.557
##    .Protists_bact     0.900    0.116    7.746    0.000    0.900    0.889
##    .Protists_sec      0.684    0.088    7.746    0.000    0.684    0.755
##    .Mites             0.931    0.120    7.746    0.000    0.931    0.910
##    .Coll              0.986    0.127    7.746    0.000    0.986    0.946
##    .Arth_mncrn_blw    0.958    0.124    7.746    0.000    0.958    0.962

## lavaan 0.6-9 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        36
##                                                       
##                                                   Used       Total
##   Number of observations                           120         150
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                17.112
##   Degrees of freedom                                 8
##   P-value (Chi-square)                           0.029
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.579    0.077    7.505    0.000    0.579    0.565
##   Protists_patho ~                                                            
##     LUI                     0.334    0.093    3.583    0.000    0.334    0.325
##     plant                   0.325    0.091    3.579    0.000    0.325    0.324
##   Microbes ~                                                                  
##     LUI                     0.300    0.082    3.659    0.000    0.300    0.302
##     plant                   0.431    0.080    5.397    0.000    0.431    0.446
##   Protists_bact ~                                                             
##     LUI                     0.300    0.107    2.789    0.005    0.300    0.309
##     plant                   0.122    0.111    1.100    0.271    0.122    0.129
##     Microbes               -0.306    0.114   -2.689    0.007   -0.306   -0.312
##   Protists_sec ~                                                              
##     LUI                     0.194    0.097    2.005    0.045    0.194    0.210
##     plant                   0.166    0.097    1.707    0.088    0.166    0.184
##     Microbes                0.181    0.102    1.776    0.076    0.181    0.194
##     Protists_bact          -0.020    0.079   -0.257    0.797   -0.020   -0.021
##   Mites ~                                                                     
##     LUI                    -0.014    0.116   -0.124    0.901   -0.014   -0.015
##     plant                   0.147    0.116    1.265    0.206    0.147    0.155
##     Microbes                0.136    0.122    1.114    0.265    0.136    0.138
##     Protists_sec           -0.119    0.108   -1.107    0.268   -0.119   -0.113
##     Protists_bact          -0.011    0.094   -0.119    0.905   -0.011   -0.011
##   Coll ~                                                                      
##     LUI                     0.035    0.118    0.296    0.767    0.035    0.035
##     plant                   0.184    0.118    1.563    0.118    0.184    0.192
##     Microbes               -0.266    0.124   -2.144    0.032   -0.266   -0.267
##     Protists_bact          -0.059    0.095   -0.614    0.539   -0.059   -0.058
##     Protists_sec           -0.057    0.110   -0.516    0.606   -0.057   -0.053
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.146    0.098    1.487    0.137    0.146    0.151
##     Mites                   0.091    0.090    1.018    0.309    0.091    0.092
##     Coll                   -0.063    0.088   -0.720    0.472   -0.063   -0.065
##     Protists_sec           -0.087    0.104   -0.841    0.400   -0.087   -0.083
##     Protists_bact           0.027    0.092    0.292    0.770    0.027    0.027
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .Protists_patho ~~                                                      
##    .Arth_mncrn_blw     0.023    0.078    0.299    0.765    0.023    0.027
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.762    0.098    7.746    0.000    0.762    0.681
##    .Protists_patho    0.756    0.098    7.746    0.000    0.756    0.671
##    .Microbes          0.583    0.075    7.746    0.000    0.583    0.557
##    .Protists_bact     0.904    0.117    7.746    0.000    0.904    0.902
##    .Protists_sec      0.685    0.088    7.746    0.000    0.685    0.754
##    .Mites             0.957    0.124    7.746    0.000    0.957    0.949
##    .Coll              0.988    0.128    7.746    0.000    0.988    0.954
##    .Arth_mncrn_blw    0.960    0.124    7.746    0.000    0.960    0.963

##            cfi          rmsea rmsea.ci.upper            bic 
##          0.956          0.097          0.162       2696.762

## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"

##            cfi          rmsea rmsea.ci.upper            bic 
##          1.000          0.000          0.085       3236.381

## quartz_off_screen 
##                 2

Use the parameters defined in the simple SEM

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

## The following object is masked from 'package:viridis':
## 
##     viridis_pal

## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.

## Run above-ground model, full model

Run below-ground model, full lui components

Use the parameters defined in the complex SEMs

get multidiv

## `set.seed(1)` was used to initialize repeatable random subsampling.

## Please record this for your records so others can reproduce.

## Try `set.seed(1); .Random.seed` for the full vector

## ...

## 50OTUs were removed because they are no longer 
## present in any sample after random subsampling

## ...

## `set.seed(1)` was used to initialize repeatable random subsampling.

## Please record this for your records so others can reproduce.

## Try `set.seed(1); .Random.seed` for the full vector

## ...

## 11288OTUs were removed because they are no longer 
## present in any sample after random subsampling

## ...

Check effect on EF MF

## [1] 496

## [1] 496

## quartz_off_screen 
##                 2

## quartz_off_screen 
##                 2

## 
##  Pearson's product-moment correlation
## 
## data:  Dim.1_all and Dim.1_fun
## t = 10.424, df = 148, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5475120 0.7343626
## sample estimates:
##       cor 
## 0.6506796

## Start:  AIC=-26.89
## Dim.1_fun ~ Dim.1_all
## 
##             Df Sum of Sq    RSS     AIC
## <none>                   122.08 -26.891
## - Dim.1_all  1     89.64 211.72  53.696

## Start:  AIC=8.5
## Dim.1_fun ~ Dim.1_mic
## 
##             Df Sum of Sq    RSS    AIC
## <none>                   154.57  8.502
## - Dim.1_mic  1    57.152 211.72 53.696

## Start:  AIC=8.77
## Dim.1_fun ~ Dim.1_plants
## 
##                Df Sum of Sq    RSS    AIC
## <none>                      154.84  8.767
## - Dim.1_plants  1    56.879 211.72 53.696

## Start:  AIC=34.29
## Dim.1_fun ~ multidiv
## 
##            Df Sum of Sq    RSS    AIC
## <none>                  183.57 34.291
## - multidiv  1    28.156 211.72 53.696

## Start:  AIC=-4.18
## Dim.1_fun ~ LUI
## 
##        Df Sum of Sq    RSS    AIC
## <none>              142.04 -4.177
## - LUI   1    69.681 211.72 53.696

##                                                Model Estimate (sd)
## 1   Functions slow-fast ~ entire community slow-fast   0.43 (0.04)
## 2             Functions slow-fast ~ plants slow-fast   0.34 (0.05)
## 3 Functions slow-fast ~ bacteria and fungi slow-fast  -0.43 (0.06)
## 4                          Functions slow-fast ~ LUI   0.68 (0.08)
## 5     Functions slow-fast ~ taxonomic multidiversity  -0.43 (0.09)
##                   Pval   R2        adj.P
## 1 2.01590737777387e-19 0.42 1.007954e-18
## 2 1.09474690131295e-11 0.26 1.368434e-11
## 3 9.58238936868961e-12 0.27 1.368434e-11
## 4 1.67387903688761e-14 0.32 4.184698e-14
## 5  4.4768570590818e-06 0.13 4.476857e-06

## lavaan 0.6-9 ended normally after 16 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         5
##                                                       
##   Number of observations                           150
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   Dim.1_fun ~                                         
##     LUI        (d)    0.261    0.092    2.823    0.005
##     Dim.1_all  (a)    0.323    0.052    6.178    0.000
##   Dim.1_all ~                                         
##     LUI        (b)    1.312    0.107   12.236    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .Dim.1_fun         0.781    0.106    7.405    0.000
##    .Dim.1_all         1.590    0.180    8.849    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     indirect          0.423    0.077    5.468    0.000
##     total             0.684    0.064   10.741    0.000
##     diff              0.163    0.158    1.029    0.303

## quartz_off_screen 
##                 2

Trait synergies

Hypotheses

Big PCA

Identification of strategy axes for each group

Plants, above- and below-ground

Bacteria & fungi

Arthropods, above-ground

Herbivores

Predators

Protists

Birds

Mites & collembola

Bats

Try to do a SEM

Run above-ground model, simple

Run below-ground model, simple

Use the parameters defined in the simple SEM

Run below-ground model, full lui components

Use the parameters defined in the complex SEMs

get multidiv

Check effect on EF MF